Bibliographic record
Abstract
Abstract The aim of this chapter is to provide you with the knowledge to be able to recognize, assess, manage, and care for people with depression in an evidence-based and person-centred way. Depression is disabling and causes significant impact upon many areas of the person’s day-to-day functioning; it is therefore important that nurses have the knowledge and skills to recognize whether someone might be depressed and know how to take the appropriate course of action. This chapter will provide a comprehensive overview of the causes and impact of depression, before exploring best practice to deliver care, as well as to prevent or to minimize further ill-health. Nursing assessments and priorities are highlighted throughout, and the nursing management of the symptoms and common health problems associated with depression can be found in Chapter 14….I lost my balance. I fell flat on my face and I couldn’t get up again. And if that implies a certain grace, a slow and easy free-fall, then you have me wrong. It was violent and painful and, above all humiliating . . . I came to understand that we are not simply fighting an illness, but the attitudes that surround it. Imagine saying to someone that you have a life-threatening illness such as cancer, and being told to pull yourself together or get over it. Imagine being terribly ill and too afraid to tell anyone lest it destroy your career. Imagine being admitted into hospital because you are too ill to function and being too ashamed to tell anyone, because it is a psychiatric hospital. Imagine telling someone that you have recently been discharged and watching them turn away, in embarrassment or disgust or fear. Bad enough to be ill, but to feel compelled to deny the very thing that, in its worst and most active state, defines you is agony indeed. (Sally Brampton (2008) in Shoot The Damn Dog.)…Sally’s experience of her depressive episode from her memoir sets the scene of just what people with depression can experience and how big an impact it can have upon their lives. We may all feel low and ‘ fed up’ at times, and often we use the term ‘ depressed’ as an adjective to describe how we are feeling in general conversation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".